Experimental and Computational Approaches to Predict Glucuronidation of Phenolics

dc.contributor.advisor

Hu, Ming

dc.creator

Wu, Baojian

dc.date.accessioned

2012-07-02T13:46:55Z

dc.date.available

2012-07-02T13:46:55Z

dc.date.created

2012-05

dc.date.issued

2012-07-02

dc.date.submitted

May 2012

dc.identifier.uri

http://hdl.handle.net/10657/ETD-UH-2012-05-269

dc.description.abstract

[Purpose] UDP-glucuronosyltransferases (UGTs) catalyze the glucuronidation reaction which has been increasingly recognized as an important metabolic and detoxification pathway. Contrasting with the functional significance of UGTs, little is known about the molecular mechanisms of how UGT recognizes its substrates. Therefore, the overall goal of this thesis is to elucidate the catalytic selectivity of human UGTs. To approach this goal, three specific aims are 1) to determine the regioselectivity of UGT1A isoforms via kinetic profiling (a) and to identify two in vitro probes for hepatic UGT1A1 based on the enzyme’s regioselectivity (b); 2) to determine the UGT1A9-mediated glucuronidation parameters for selected flavonols (n=30) and establish a pharmacophore-based in silico model using this dataset; 3) to determine UGT1A9-mediated glucuronidation parameters for a large class of structurally diverse phenolics (n=145) and to establish a more generalized in silico model based on this large database.
[Methods] In the absence of a three-dimensional structure of the full-length enzyme, we employed a ligand-based QSAR approach in combination with experimentally determined “expert” knowledge. “Expert” knowledge was incorporated into a model to tackle the challenges in model construction and to ensure model quality. On the experimental side, the interaction of UGT-substrate was characterized by kinetic determination, which involves measuring glucuronidation rates at various concentrations of a substrate, and deriving the kinetic parameters (Km, Vmax, and CLint) by model fitting. On the in silico side, powerful QSAR techniques (Comparative Molecular Field Analysis (CoMFA) and/or Comparative Molecular Similarity Indices Analysis (CoMSIA)) combined with protein homology modeling were used to analyze the structure-activity relationships and uncover the structural features for good or poor UGT substrates.
[Results] 1a) UGT1A1 and 1A3 regioselectively metabolize 7-OH of flavonols, whereas UGT1A7, 1A8, 1A9 and 1A10 prefer to glucuronidate 3-OH group. UGT1A1 and 1A9 are the most efficient conjugating enzymes with Km values of ≤1 µM. 1b) UGT1A1 and 1A9 are the main isoforms for glucuronidating the two flavonoid probes 3,3’,4’-trihydroxyflavone (33’4’THF) and 3,6,4’-trihydroxyflavone (364’THF), where UGT1A1 accounts for 92 ± 7 % and 91 ± 10 % of 4’-O-glucuronidation of 33’4’THF and 364’THF, respectively, and UGT1A9 accounts for most of the 3-O-glucuronidation. Highly significant correlations (R2 > 0.944, p < 0.0001) between the rates of flavonoids 4’-O-glucuronidation and that of estradiol-3-glucuronidation or SN-38 glucuronidation are observed across 12 human liver microsomes (HLMs). 2) The derived CoMFA models for 30 flavonols possess good internal and external consistency and show statistical significance and substantive predictive abilities (Vmax model: q2 = 0.738, r2= 0.976, r2pred = 0.735; CLint model: q2 = 0.561, r2= 0.938, r2pred = 0.630). The contour maps derived from CoMFA modeling clearly indicate structural characteristics associated with rapid or slow 3-O-glucuronidation of flavonols. 3) The 3D-QSAR analyses based on 145 phenolics produce statistically reliable models with good predictive power (CoMFA: q2 = 0.548, r2= 0.949, r2pred = 0.775; CoMSIA: q2 = 0.579, r2= 0.876, r2pred = 0.700). The contour coefficient maps generated from CoMFA/CoMSIA are applied to elucidate structural features among substrates that are responsible for the selectivity differences. Furthermore, the contour coefficient maps are overlaid in the catalytic pocket of a homology model of UGT1A9; this enabled us to identify the UGT1A9 catalytic pocket with a high degree of confidence.
[Conclusion] The extensive kinetic characterization on the formation of multiple glucuronides from a UGT substrate indicates that multiple distinct binding modes within the catalytic domain are possible for a substrate molecule. Interestingly, in our study, formation of 4’-O-glucuronides from 33’4’THF and 364’THF is proven to be the excellent markers for hepatic UGT1A1, and for UGT1A1-mediated glucuronidation of SN-38. Further, we for the first time demonstrate that the approach of coupling CoMFA analysis with a pharmacophore-based structural alignment is viable for constructing a predictive model for regiospecific glucuronidation of flavonols by UGT1A9. In addition, based on a large set of structurally diverse molecules (including those with multiple glucuronidation sites), the 3D-QSAR techniques CoMFA/CoMSIA can be used to predict the substrate selectivity of UGT1A9. Our findings also provide a possible molecular basis for understanding UGT1A9 functions and its substrate selectivity.

dc.format.mimetype

application/pdf

dc.language.iso

eng

dc.subject

UGTs, Glucuronidation, In silico model, QSAR, Phenolics, Flavonoids

dc.title

Experimental and Computational Approaches to Predict Glucuronidation of Phenolics